Retrievers are a key bottleneck in Temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream generation, regardless of LLM reasoning. We propose Temporal-aware Matryoshka Representation Learning (TMRL), an efficient method that equips retrievers with temporal-aware Matryoshka embeddings. TMRL leverages the nested structure of Matryoshka embeddings to introduce a temporal subspace, enhancing temporal encoding while preserving general semantic representations. Experiments show that TMRL efficiently adapts diverse text embedding models, achieving competitive temporal retrieval and temporal RAG performance compared to prior Matryoshka-based non-temporal methods and prior temporal methods, while enabling flexible accuracy-efficiency trade-offs.

翻译：检索器是时序检索增强生成系统中的关键瓶颈：无论大语言模型的推理能力如何，若未能检索到时序相关的上下文，都会降低下游生成质量。本文提出时序感知嵌套表示学习方法，这是一种高效的技术，通过为检索器配备时序感知的嵌套嵌入来应对上述挑战。该方法利用嵌套嵌入的层次化结构引入时序子空间，在保持通用语义表示的同时增强时序编码能力。实验表明，TMRL能够高效适配多种文本嵌入模型，在时序检索和时序RAG任务上，相较于先前的非时序嵌套方法及时序方法均展现出竞争优势，同时支持灵活的精度-效率权衡。